94 research outputs found
Exact Recovery of Sparse Signals via Orthogonal Matching Pursuit: How Many Iterations Do We Need?
Orthogonal matching pursuit (OMP) is a greedy algorithm widely used for the
recovery of sparse signals from compressed measurements. In this paper, we
analyze the number of iterations required for the OMP algorithm to perform
exact recovery of sparse signals. Our analysis shows that OMP can accurately
recover all -sparse signals within iterations when the
measurement matrix satisfies a restricted isometry property (RIP). Our result
improves upon the recent result of Zhang and also bridges the gap between
Zhang's result and the fundamental limit of OMP at which exact recovery of
-sparse signals cannot be uniformly guaranteed
Sparse Detection of Non-Sparse Signals for Large-Scale Wireless Systems
In this paper, we introduce a new detection algorithm for large-scale
wireless systems, referred to as post sparse error detection (PSED) algorithm,
that employs a sparse error recovery algorithm to refine the estimate of a
symbol vector obtained by the conventional linear detector. The PSED algorithm
operates in two steps: 1) sparse transformation converting the original
non-sparse system into the sparse system whose input is an error vector caused
by the symbol slicing and 2) estimation of the error vector using the sparse
recovery algorithm. From the asymptotic mean square error (MSE) analysis and
empirical simulations performed on large-scale systems, we show that the PSED
algorithm brings significant performance gain over classical linear detectors
while imposing relatively small computational overhead
Statistical Recovery of Simultaneously Sparse Time-Varying Signals from Multiple Measurement Vectors
In this paper, we propose a new sparse signal recovery algorithm, referred to
as sparse Kalman tree search (sKTS), that provides a robust reconstruction of
the sparse vector when the sequence of correlated observation vectors are
available. The proposed sKTS algorithm builds on expectation-maximization (EM)
algorithm and consists of two main operations: 1) Kalman smoothing to obtain
the a posteriori statistics of the source signal vectors and 2) greedy tree
search to estimate the support of the signal vectors. Through numerical
experiments, we demonstrate that the proposed sKTS algorithm is effective in
recovering the sparse signals and performs close to the Oracle (genie-based)
Kalman estimator
Multipath Matching Pursuit
In this paper, we propose an algorithm referred to as multipath matching
pursuit that investigates multiple promising candidates to recover sparse
signals from compressed measurements. Our method is inspired by the fact that
the problem to find the candidate that minimizes the residual is readily
modeled as a combinatoric tree search problem and the greedy search strategy is
a good fit for solving this problem. In the empirical results as well as the
restricted isometry property (RIP) based performance guarantee, we show that
the proposed MMP algorithm is effective in reconstructing original sparse
signals for both noiseless and noisy scenarios.Comment: To appear in IEEE Transactions on Information Theor
Sparse Vector Coding for Ultra-Reliable and Low Latency Communications
Ultra reliable and low latency communication (URLLC) is a newly introduced
service category in 5G to support delay-sensitive applications. In order to
support this new service category, 3rd Generation Partnership Project (3GPP)
sets an aggressive requirement that a packet should be delivered with 10^-5
packet error rate within 1 ms transmission period. Since the current wireless
transmission scheme designed to maximize the coding gain by transmitting
capacity achieving long codeblock is not relevant for this purpose, a new
transmission scheme to support URLLC is required. In this paper, we propose a
new approach to support the short packet transmission, called sparse vector
coding (SVC). Key idea behind the proposed SVC technique is to transmit the
information after the sparse vector transformation. By mapping the information
into the position of nonzero elements and then transmitting it after the random
spreading, we obtain an underdetermined sparse system for which the principle
of compressed sensing can be applied. From the numerical evaluations and
performance analysis, we demonstrate that the proposed SVC technique is very
effective in URLLC transmission and outperforms the 4G LTE and LTE-Advanced
scheme.Comment: To appear in IEEE Transactions on Wireless Communications. Copyright
2018 IEEE. Personal use of this material is permitted. Permission from IEEE
must be obtained for all other use
Optimal Power Control for Transmitting Correlated Sources with Energy Harvesting Constraints
We investigate the weighted-sum distortion minimization problem in
transmitting two correlated Gaussian sources over Gaussian channels using two
energy harvesting nodes. To this end, we develop offline and online power
control policies to optimize the transmit power of the two nodes. In the
offline case, we cast the problem as a convex optimization and investigate the
structure of the optimal solution. We also develop a generalized water-filling
based power allocation algorithm to obtain the optimal solution efficiently.
For the online case, we quantify the distortion of the system using a cost
function and show that the expected cost equals the expected weighted-sum
distortion. Based on Banach's fixed point theorem, we further propose a
geometrically converging algorithm to find the minimum cost via simple
iterations. Simulation results show that our online power control outperforms
the greedy power control where each node uses all the available energy in each
slot and performs close to that of the proposed offline power control.
Moreover, the performance of our offline power control almost coincides with
the performance limit of the system.Comment: 15 pages, 12 figure
Greedy Sparse Signal Recovery with Tree Pruning
Recently, greedy algorithm has received much attention as a cost-effective
means to reconstruct the sparse signals from compressed measurements. Much of
previous work has focused on the investigation of a single candidate to
identify the support (index set of nonzero elements) of the sparse signals.
Well-known drawback of the greedy approach is that the chosen candidate is
often not the optimal solution due to the myopic decision in each iteration. In
this paper, we propose a greedy sparse recovery algorithm investigating
multiple promising candidates via the tree search. Two key ingredients of the
proposed algorithm, referred to as the matching pursuit with a tree pruning
(TMP), to achieve efficiency in the tree search are the {\it pre-selection} to
put a restriction on columns of the sensing matrix to be investigated and the
{\it tree pruning} to eliminate unpromising paths from the search tree. In our
performance guarantee analysis and empirical simulations, we show that TMP is
effective in recovering sparse signals in both noiseless and noisy scenarios.Comment: 29 pages, 8 figures, draftcls, 11pt
Structured Compressive Sensing Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO
Massive MIMO is a promising technique for future 5G communications due to its
high spectrum and energy efficiency. To realize its potential performance gain,
accurate channel estimation is essential. However, due to massive number of
antennas at the base station (BS), the pilot overhead required by conventional
channel estimation schemes will be unaffordable, especially for frequency
division duplex (FDD) massive MIMO. To overcome this problem, we propose a
structured compressive sensing (SCS)-based spatio-temporal joint channel
estimation scheme to reduce the required pilot overhead, whereby the
spatio-temporal common sparsity of delay-domain MIMO channels is leveraged.
Particularly, we first propose the non-orthogonal pilots at the BS under the
framework of CS theory to reduce the required pilot overhead. Then, an adaptive
structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly
estimate channels associated with multiple OFDM symbols from the limited number
of pilots, whereby the spatio-temporal common sparsity of MIMO channels is
exploited to improve the channel estimation accuracy. Moreover, by exploiting
the temporal channel correlation, we propose a space-time adaptive pilot scheme
to further reduce the pilot overhead. Additionally, we discuss the proposed
channel estimation scheme in multi-cell scenario. Simulation results
demonstrate that the proposed scheme can accurately estimate channels with the
reduced pilot overhead, and it is capable of approaching the optimal oracle
least squares estimator.Comment: 16 pages; 12 figures;submitted to IEEE Trans. Communication
Joint Channel Training and Feedback for FDD Massive MIMO Systems
Massive multiple-input multiple-output (MIMO) is widely recognized as a
promising technology for future 5G wireless communication systems. To achieve
the theoretical performance gains in massive MIMO systems, accurate channel
state information at the transmitter (CSIT) is crucial. Due to the overwhelming
pilot signaling and channel feedback overhead, however, conventional downlink
channel estimation and uplink channel feedback schemes might not be suitable
for frequency-division duplexing (FDD) massive MIMO systems. In addition, these
two topics are usually separately considered in the literature. In this paper,
we propose a joint channel training and feedback scheme for FDD massive MIMO
systems. Specifically, we firstly exploit the temporal correlation of
time-varying channels to propose a differential channel training and feedback
scheme, which simultaneously reduces the overhead for downlink training and
uplink feedback. We next propose a structured compressive sampling matching
pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the
structured sparsity of wireless MIMO channels. Simulation results demonstrate
that the proposed scheme can achieve substantial reduction in the training and
feedback overhead
On the Fundamental Recovery Limit of Orthogonal Least Squares
Orthogonal least squares (OLS) is a classic algorithm for sparse recovery,
function approximation, and subset selection. In this paper, we analyze the
performance guarantee of the OLS algorithm. Specifically, we show that OLS
guarantees the exact reconstruction of any -sparse vector in iterations,
provided that a sensing matrix has unit -norm columns and satisfies
the restricted isometry property (RIP) of order with \begin{align*}
\delta_{K+1} &<C_{K} = \begin{cases} \frac{1}{\sqrt{K}}, & K=1, \\
\frac{1}{\sqrt{K+\frac{1}{4}}}, & K=2, \\ \frac{1}{\sqrt{K+\frac{1}{16}}}, &
K=3, \\ \frac{1}{\sqrt{K}}, & K \ge 4. \end{cases} \end{align*} Furthermore, we
show that the proposed guarantee is optimal in the sense that if , then there exists a counterexample for which OLS fails the
recovery
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